Overview

Dataset statistics

Number of variables11
Number of observations22483955
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 GiB
Average record size in memory88.0 B

Variable types

Numeric11

Alerts

LongitudAcc is highly correlated with Fuel Rate and 2 other fieldsHigh correlation
EngineSpeed is highly correlated with EngineAirInletPressure and 2 other fieldsHigh correlation
Fuel Rate is highly correlated with Engine Load and 2 other fieldsHigh correlation
Engine Load is highly correlated with Boost Pressure and 2 other fieldsHigh correlation
Boost Pressure is highly correlated with Engine Load and 2 other fieldsHigh correlation
EngineAirInletPressure is highly correlated with EngineSpeed and 3 other fieldsHigh correlation
AcceleratorPedalPos is highly correlated with EngineSpeed and 4 other fieldsHigh correlation
VehicleSpeed is highly correlated with EngineSpeedHigh correlation
BrakePedalPos is highly correlated with AcceleratorPedalPosHigh correlation
Fuel Rate is highly skewed (γ1 = 50.74352758) Skewed
Timestamp has unique values Unique
LongitudAcc has 5253570 (23.4%) zeros Zeros
EngineSpeed has 401981 (1.8%) zeros Zeros
Fuel Rate has 5140033 (22.9%) zeros Zeros
Engine Load has 5163040 (23.0%) zeros Zeros
Boost Pressure has 1474517 (6.6%) zeros Zeros
AcceleratorPedalPos has 8840773 (39.3%) zeros Zeros
VehicleSpeed has 3187013 (14.2%) zeros Zeros
BrakePedalPos has 18270026 (81.3%) zeros Zeros

Reproduction

Analysis started2022-11-23 14:53:52.714253
Analysis finished2022-11-23 15:21:46.290000
Duration27 minutes and 53.58 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Timestamp
Real number (ℝ≥0)

UNIQUE

Distinct22483955
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.874165907 × 1010
Minimum1.98526209 × 1010
Maximum1.117845807 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.5 MiB
2022-11-23T16:21:46.643820image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1.98526209 × 1010
5-th percentile2.637722905 × 1010
Q14.899890384 × 1010
median7.091272126 × 1010
Q39.187471026 × 1010
95-th percentile1.081962579 × 1011
Maximum1.117845807 × 1011
Range9.193195981 × 1010
Interquartile range (IQR)4.287580643 × 1010

Descriptive statistics

Standard deviation2.663656995 × 1010
Coefficient of variation (CV)0.3874880285
Kurtosis-1.157031879
Mean6.874165907 × 1010
Median Absolute Deviation (MAD)2.14590836 × 1010
Skewness-0.1623286515
Sum1.545584369 × 1018
Variance7.095068586 × 1020
MonotonicityStrictly increasing
2022-11-23T16:21:46.797714image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.98526209 × 10101
 
< 0.1%
8.467566757 × 10101
 
< 0.1%
8.467567574 × 10101
 
< 0.1%
8.467567465 × 10101
 
< 0.1%
8.467567378 × 10101
 
< 0.1%
8.467567273 × 10101
 
< 0.1%
8.467567171 × 10101
 
< 0.1%
8.467567063 × 10101
 
< 0.1%
8.467566978 × 10101
 
< 0.1%
8.46756687 × 10101
 
< 0.1%
Other values (22483945)22483945
> 99.9%
ValueCountFrequency (%)
1.98526209 × 10101
< 0.1%
1.985262163 × 10101
< 0.1%
1.98526228 × 10101
< 0.1%
1.985262399 × 10101
< 0.1%
1.985262466 × 10101
< 0.1%
1.985262586 × 10101
< 0.1%
1.985262699 × 10101
< 0.1%
1.985262866 × 10101
< 0.1%
1.985262986 × 10101
< 0.1%
1.985263061 × 10101
< 0.1%
ValueCountFrequency (%)
1.117845807 × 10111
< 0.1%
1.117845798 × 10111
< 0.1%
1.117845787 × 10111
< 0.1%
1.117845776 × 10111
< 0.1%
1.117845768 × 10111
< 0.1%
1.117845757 × 10111
< 0.1%
1.117845746 × 10111
< 0.1%
1.117845738 × 10111
< 0.1%
1.117845727 × 10111
< 0.1%
1.117845716 × 10111
< 0.1%

WetTankAirPressure
Real number (ℝ≥0)

Distinct205
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.97261414
Minimum0
Maximum14.0658
Zeros62805
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size171.5 MiB
2022-11-23T16:21:46.953122image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.7909
Q110.4804
median11.10095
Q311.65255
95-th percentile12.20415
Maximum14.0658
Range14.0658
Interquartile range (IQR)1.17215

Descriptive statistics

Standard deviation1.096493401
Coefficient of variation (CV)0.09993000637
Kurtosis37.85479854
Mean10.97261414
Median Absolute Deviation (MAD)0.5516
Skewness-4.468134083
Sum246707762.6
Variance1.202297779
MonotonicityNot monotonic
2022-11-23T16:21:47.092267image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.8941783203
 
3.5%
11.032776894
 
3.5%
10.82515763826
 
3.4%
11.10095758912
 
3.4%
11.1699751705
 
3.3%
10.7562733935
 
3.3%
11.3078724007
 
3.2%
11.4457714184
 
3.2%
11.51465707858
 
3.1%
11.37675707232
 
3.1%
Other values (195)15062199
67.0%
ValueCountFrequency (%)
062805
0.3%
0.068951185
 
< 0.1%
0.1379613
 
< 0.1%
0.206851437
 
< 0.1%
0.2758318
 
< 0.1%
0.34475407
 
< 0.1%
0.4137493
 
< 0.1%
0.48265416
 
< 0.1%
0.5516376
 
< 0.1%
0.62055497
 
< 0.1%
ValueCountFrequency (%)
14.06582
 
< 0.1%
13.996851
 
< 0.1%
13.92794
 
< 0.1%
13.858953
 
< 0.1%
13.7911
 
< 0.1%
13.7210511
 
< 0.1%
13.652126
 
< 0.1%
13.5831546
< 0.1%
13.514274
< 0.1%
13.44525106
< 0.1%

LongitudAcc
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct139
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01471261173
Minimum-10
Maximum13
Zeros5253570
Zeros (%)23.4%
Negative9283477
Negative (%)41.3%
Memory size171.5 MiB
2022-11-23T16:21:47.250086image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile-1
Q1-0.2
median0
Q30.2
95-th percentile0.8
Maximum13
Range23
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.9786698016
Coefficient of variation (CV)66.51910754
Kurtosis125.3726755
Mean0.01471261173
Median Absolute Deviation (MAD)0.2
Skewness9.528671171
Sum330797.7
Variance0.9577945806
MonotonicityNot monotonic
2022-11-23T16:21:47.396027image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05253570
23.4%
-0.12196076
9.8%
-0.21876597
 
8.3%
0.11869076
 
8.3%
0.21405793
 
6.3%
-0.31348372
 
6.0%
0.31054275
 
4.7%
-0.4927723
 
4.1%
0.4764429
 
3.4%
0.5617489
 
2.7%
Other values (129)5170555
23.0%
ValueCountFrequency (%)
-101
 
< 0.1%
-91
 
< 0.1%
-8.61
 
< 0.1%
-8.31
 
< 0.1%
-7.91
 
< 0.1%
-7.71
 
< 0.1%
-7.33
< 0.1%
-7.21
 
< 0.1%
-7.11
 
< 0.1%
-74
< 0.1%
ValueCountFrequency (%)
1382274
0.4%
12.910823
 
< 0.1%
5.91
 
< 0.1%
5.81
 
< 0.1%
5.51
 
< 0.1%
5.42
 
< 0.1%
5.34
 
< 0.1%
5.210
 
< 0.1%
5.111
 
< 0.1%
515
 
< 0.1%

EngineSpeed
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct13871
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1072.689465
Minimum0
Maximum8191.875
Zeros401981
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size171.5 MiB
2022-11-23T16:21:47.551749image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile593.625
Q1892.625
median1159
Q31288
95-th percentile1462.875
Maximum8191.875
Range8191.875
Interquartile range (IQR)395.375

Descriptive statistics

Standard deviation322.250278
Coefficient of variation (CV)0.3004133895
Kurtosis4.392435126
Mean1072.689465
Median Absolute Deviation (MAD)158.5
Skewness-0.7061916685
Sum2.411830165 × 1010
Variance103845.2417
MonotonicityNot monotonic
2022-11-23T16:21:47.690632image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0401981
 
1.8%
60068722
 
0.3%
600.2568589
 
0.3%
599.7567429
 
0.3%
600.565542
 
0.3%
599.565346
 
0.3%
599.2561294
 
0.3%
600.87559397
 
0.3%
59956840
 
0.3%
600.12556135
 
0.2%
Other values (13861)21512680
95.7%
ValueCountFrequency (%)
0401981
1.8%
15.251
 
< 0.1%
15.8751
 
< 0.1%
17.251
 
< 0.1%
17.8751
 
< 0.1%
18.252
 
< 0.1%
18.8751
 
< 0.1%
19.6251
 
< 0.1%
201
 
< 0.1%
211
 
< 0.1%
ValueCountFrequency (%)
8191.875350
< 0.1%
2230.6251
 
< 0.1%
2227.6251
 
< 0.1%
2226.1251
 
< 0.1%
2210.3751
 
< 0.1%
2195.3751
 
< 0.1%
2183.3751
 
< 0.1%
2169.251
 
< 0.1%
2158.8752
 
< 0.1%
21571
 
< 0.1%

Fuel Rate
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1103
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.28743578
Minimum0
Maximum3876.198645
Zeros5140033
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size171.5 MiB
2022-11-23T16:21:47.844023image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.537822
median7.984845
Q321.706949
95-th percentile48.263952
Maximum3876.198645
Range3876.198645
Interquartile range (IQR)20.169127

Descriptive statistics

Standard deviation72.64006638
Coefficient of variation (CV)4.751618744
Kurtosis2693.574083
Mean15.28743578
Median Absolute Deviation (MAD)7.984845
Skewness50.74352758
Sum343722018.1
Variance5276.579243
MonotonicityNot monotonic
2022-11-23T16:21:47.987442image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05140033
 
22.9%
3.844555166583
 
0.7%
3.903702159101
 
0.7%
3.785408153193
 
0.7%
3.962849141634
 
0.6%
3.253085131346
 
0.6%
3.726261126208
 
0.6%
3.193938125588
 
0.6%
3.312232123136
 
0.5%
4.021996119381
 
0.5%
Other values (1093)16097752
71.6%
ValueCountFrequency (%)
05140033
22.9%
0.05914721547
 
0.1%
0.11829420927
 
0.1%
0.17744125702
 
0.1%
0.23658832903
 
0.1%
0.29573530165
 
0.1%
0.35488226707
 
0.1%
0.41402924411
 
0.1%
0.47317621133
 
0.1%
0.53232318387
 
0.1%
ValueCountFrequency (%)
3876.1986457596
< 0.1%
3643.0411711
 
< 0.1%
65.061744
 
< 0.1%
65.00255382
 
< 0.1%
64.943406154
 
< 0.1%
64.884259162
 
< 0.1%
64.825112120
 
< 0.1%
64.765965145
 
< 0.1%
64.706818205
 
< 0.1%
64.647671132
 
< 0.1%

Engine Load
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct201
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.86648245
Minimum0
Maximum100
Zeros5163040
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size171.5 MiB
2022-11-23T16:21:48.148221image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median25
Q345.5
95-th percentile93
Maximum100
Range100
Interquartile range (IQR)41.5

Descriptive statistics

Standard deviation28.10432456
Coefficient of variation (CV)0.9105127092
Kurtosis-0.0253281456
Mean30.86648245
Median Absolute Deviation (MAD)20.5
Skewness0.8693444933
Sum694000602.5
Variance789.853059
MonotonicityNot monotonic
2022-11-23T16:21:48.737413image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05163040
 
23.0%
100738391
 
3.3%
22.5322595
 
1.4%
23301261
 
1.3%
22288452
 
1.3%
23.5265812
 
1.2%
19253565
 
1.1%
21.5242857
 
1.1%
24239906
 
1.1%
18.5239370
 
1.1%
Other values (191)14428706
64.2%
ValueCountFrequency (%)
05163040
23.0%
0.591609
 
0.4%
170251
 
0.3%
1.555125
 
0.2%
251482
 
0.2%
2.547148
 
0.2%
349573
 
0.2%
3.547338
 
0.2%
451994
 
0.2%
4.548573
 
0.2%
ValueCountFrequency (%)
100738391
3.3%
99.523649
 
0.1%
9929307
 
0.1%
98.536721
 
0.2%
9831035
 
0.1%
97.529328
 
0.1%
9727089
 
0.1%
96.526489
 
0.1%
9626683
 
0.1%
95.525350
 
0.1%

Boost Pressure
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct198
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2409602234
Minimum0
Maximum1.697746
Zeros1474517
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size171.5 MiB
2022-11-23T16:21:48.886592image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.051708
median0.12927
Q30.336102
95-th percentile0.870418
Maximum1.697746
Range1.697746
Interquartile range (IQR)0.284394

Descriptive statistics

Standard deviation0.2858978553
Coefficient of variation (CV)1.186493983
Kurtosis3.745014936
Mean0.2409602234
Median Absolute Deviation (MAD)0.112034
Skewness1.922574278
Sum5417738.819
Variance0.08173758365
MonotonicityNot monotonic
2022-11-23T16:21:49.024853image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0086181797794
 
8.0%
01474517
 
6.6%
0.094798868531
 
3.9%
0.017236836343
 
3.7%
0.08618829829
 
3.7%
0.103416817354
 
3.6%
0.077562701942
 
3.1%
0.112034698850
 
3.1%
0.120652565666
 
2.5%
0.068944545634
 
2.4%
Other values (188)13347495
59.4%
ValueCountFrequency (%)
01474517
6.6%
0.0086181797794
8.0%
0.017236836343
3.7%
0.025854517874
 
2.3%
0.034472418576
 
1.9%
0.04309377647
 
1.7%
0.051708367797
 
1.6%
0.060326421476
 
1.9%
0.068944545634
 
2.4%
0.077562701942
 
3.1%
ValueCountFrequency (%)
1.6977462
 
< 0.1%
1.6891283
 
< 0.1%
1.6805112
 
< 0.1%
1.67189212
 
< 0.1%
1.66327416
< 0.1%
1.65465618
< 0.1%
1.64603812
 
< 0.1%
1.6374219
< 0.1%
1.62880221
< 0.1%
1.62018432
< 0.1%

EngineAirInletPressure
Real number (ℝ≥0)

HIGH CORRELATION

Distinct102
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.267869
Minimum34
Maximum510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size171.5 MiB
2022-11-23T16:21:49.171082image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile102
Q1106
median114
Q3134
95-th percentile188
Maximum510
Range476
Interquartile range (IQR)28

Descriptive statistics

Standard deviation28.66608806
Coefficient of variation (CV)0.2288383149
Kurtosis4.227496827
Mean125.267869
Median Absolute Deviation (MAD)10
Skewness1.943822648
Sum2816517130
Variance821.7446044
MonotonicityNot monotonic
2022-11-23T16:21:49.330035image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1022433311
 
10.8%
1101826591
 
8.1%
1121699190
 
7.6%
1041463657
 
6.5%
1081275522
 
5.7%
1141130552
 
5.0%
106956562
 
4.3%
116873347
 
3.9%
100811680
 
3.6%
118660129
 
2.9%
Other values (92)9353414
41.6%
ValueCountFrequency (%)
3414
< 0.1%
505
 
< 0.1%
5210
< 0.1%
663
 
< 0.1%
6820
< 0.1%
704
 
< 0.1%
822
 
< 0.1%
8420
< 0.1%
8613
< 0.1%
921
 
< 0.1%
ValueCountFrequency (%)
510368
 
< 0.1%
50811
 
< 0.1%
2723
 
< 0.1%
27014
 
< 0.1%
26840
 
< 0.1%
26639
 
< 0.1%
26467
 
< 0.1%
262185
 
< 0.1%
260535
< 0.1%
2581276
< 0.1%

AcceleratorPedalPos
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct251
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.84106355
Minimum0
Maximum100
Zeros8840773
Zeros (%)39.3%
Negative0
Negative (%)0.0%
Memory size171.5 MiB
2022-11-23T16:21:49.496907image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median40.8
Q367.2
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)67.2

Descriptive statistics

Standard deviation35.4232917
Coefficient of variation (CV)0.9361071908
Kurtosis-1.41225725
Mean37.84106355
Median Absolute Deviation (MAD)40.8
Skewness0.2272119187
Sum850816770
Variance1254.809595
MonotonicityNot monotonic
2022-11-23T16:21:49.655857image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08840773
39.3%
1001209694
 
5.4%
62106055
 
0.5%
59.6104698
 
0.5%
65.2104646
 
0.5%
62.4104562
 
0.5%
63.6104307
 
0.5%
61.2104002
 
0.5%
60.8103702
 
0.5%
64103100
 
0.5%
Other values (241)11598416
51.6%
ValueCountFrequency (%)
08840773
39.3%
0.48847
 
< 0.1%
0.89062
 
< 0.1%
1.29173
 
< 0.1%
1.69125
 
< 0.1%
29324
 
< 0.1%
2.49322
 
< 0.1%
2.89957
 
< 0.1%
3.29626
 
< 0.1%
3.610014
 
< 0.1%
ValueCountFrequency (%)
1001209694
5.4%
99.631026
 
0.1%
99.230090
 
0.1%
98.830592
 
0.1%
98.431648
 
0.1%
9831407
 
0.1%
97.631924
 
0.1%
97.232373
 
0.1%
96.832153
 
0.1%
96.433273
 
0.1%

VehicleSpeed
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1080
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.22274917
Minimum0
Maximum255.97971
Zeros3187013
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size171.5 MiB
2022-11-23T16:21:49.802887image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.397388
median39.095154
Q356.594034
95-th percentile75.592818
Maximum255.97971
Range255.97971
Interquartile range (IQR)40.196646

Descriptive statistics

Standard deviation24.93746284
Coefficient of variation (CV)0.669952204
Kurtosis0.5183566949
Mean37.22274917
Median Absolute Deviation (MAD)20.09637
Skewness0.1709047565
Sum836914617.2
Variance621.877053
MonotonicityNot monotonic
2022-11-23T16:21:49.964876image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03187013
 
14.2%
48.49689638861
 
0.2%
47.79381637822
 
0.2%
47.39540437681
 
0.2%
47.5946137391
 
0.2%
46.99699237328
 
0.2%
48.09457837304
 
0.2%
47.19619837274
 
0.2%
49.996837115
 
0.2%
50.0944536983
 
0.2%
Other values (1070)18959183
84.3%
ValueCountFrequency (%)
03187013
14.2%
0.9999365072
 
< 0.1%
1.0975865987
 
< 0.1%
1.1991427236
 
< 0.1%
1.2967927799
 
< 0.1%
1.3983488275
 
< 0.1%
1.4999049102
 
< 0.1%
1.59755412402
 
0.1%
1.699119696
 
< 0.1%
1.7967610142
 
< 0.1%
ValueCountFrequency (%)
255.97971346
 
< 0.1%
255.9758046019
< 0.1%
112.2896881
 
< 0.1%
112.1920381
 
< 0.1%
112.0904821
 
< 0.1%
111.5905141
 
< 0.1%
111.3913082
 
< 0.1%
111.2897521
 
< 0.1%
111.1921022
 
< 0.1%
111.0905462
 
< 0.1%

BrakePedalPos
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct244
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.437663454
Minimum0
Maximum97.2
Zeros18270026
Zeros (%)81.3%
Negative0
Negative (%)0.0%
Memory size171.5 MiB
2022-11-23T16:21:50.139035image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile22.4
Maximum97.2
Range97.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.869066842
Coefficient of variation (CV)2.289074235
Kurtosis4.764316588
Mean3.437663454
Median Absolute Deviation (MAD)0
Skewness2.250963228
Sum77292270.4
Variance61.92221296
MonotonicityNot monotonic
2022-11-23T16:21:50.296478image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018270026
81.3%
15.6182889
 
0.8%
16173874
 
0.8%
16.4149439
 
0.7%
16.8148079
 
0.7%
17.2135796
 
0.6%
15.2132112
 
0.6%
17.6125249
 
0.6%
18107043
 
0.5%
20.893141
 
0.4%
Other values (234)2966307
 
13.2%
ValueCountFrequency (%)
018270026
81.3%
0.457112
 
0.3%
0.829574
 
0.1%
1.218586
 
0.1%
1.616743
 
0.1%
215114
 
0.1%
2.416156
 
0.1%
2.815778
 
0.1%
3.214263
 
0.1%
3.615520
 
0.1%
ValueCountFrequency (%)
97.282
 
< 0.1%
96.8374
< 0.1%
96.420
 
< 0.1%
966
 
< 0.1%
95.67
 
< 0.1%
95.220
 
< 0.1%
94.828
 
< 0.1%
94.428
 
< 0.1%
949
 
< 0.1%
93.66
 
< 0.1%

Interactions

2022-11-23T16:20:01.052682image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:11:12.852180image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:12:06.270522image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:12:56.977111image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:13:51.935194image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:14:47.457390image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:15:38.525775image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:16:32.775983image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:17:25.584285image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:18:17.736045image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:19:10.217496image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:20:05.647311image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:11:17.724612image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:12:10.669857image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:13:01.601268image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:13:57.446283image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:14:52.013613image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:15:43.560737image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:16:37.584161image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:17:30.307214image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:18:22.543571image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:19:14.808497image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:20:10.269899image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:11:22.669623image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:12:15.354152image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:13:06.162672image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:14:02.315986image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:14:56.787588image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:15:48.632432image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:16:42.418727image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:17:35.059688image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:18:27.381752image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:19:19.453537image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T16:14:06.916324image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T16:18:08.219083image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:19:00.861344image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:19:51.879091image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:20:47.044848image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:12:01.752115image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:12:52.330149image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:13:43.514771image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:14:42.933980image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:15:33.372887image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:16:27.968248image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:17:20.868840image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:18:12.959310image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:19:05.638404image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:19:56.492723image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2022-11-23T16:21:50.449033image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-23T16:21:50.683633image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-23T16:21:50.931808image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-23T16:21:51.184834image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-23T16:21:51.469016image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-23T16:20:47.672780image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-23T16:20:57.425487image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TimestampWetTankAirPressureLongitudAccEngineSpeedFuel RateEngine LoadBoost PressureEngineAirInletPressureAcceleratorPedalPosVehicleSpeedBrakePedalPos
01.985262e+106.274450.0598.1256.68361138.50.0102.00.00.00.0
11.985262e+106.274450.0606.2506.80190538.50.0104.00.00.00.0
21.985262e+106.274450.0601.8756.74275838.50.0102.00.00.00.0
31.985262e+106.274450.0600.3756.80190539.00.0102.00.00.00.0
41.985262e+106.274450.0596.3756.56531737.50.0102.00.00.00.0
51.985263e+106.274450.0602.2506.56531737.50.0102.00.00.00.0
61.985263e+106.274450.0601.0006.68361138.00.0102.00.00.00.0
71.985263e+106.343400.0597.8756.74275838.50.0102.00.00.00.0
81.985263e+106.343400.0597.2506.62446438.00.0102.00.00.00.0
91.985263e+106.343400.0604.5006.80190539.00.0102.00.00.00.0

Last rows

TimestampWetTankAirPressureLongitudAccEngineSpeedFuel RateEngine LoadBoost PressureEngineAirInletPressureAcceleratorPedalPosVehicleSpeedBrakePedalPos
224839451.117846e+1110.27355-0.1615.6254.02199622.00.017236104.00.02.6990461.6
224839461.117846e+1110.34250-0.3600.1253.90370222.50.017236104.00.02.0975220.4
224839471.117846e+1110.41145-0.3596.5004.43602526.00.017236104.00.01.3983480.0
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